{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 比亚迪股票价格走势分析与可视化\n",
    "\n",
    "本Notebook实现以下分析任务：\n",
    "1. 读取数据，观察数据形状，输出前5行，并进行描述性分析\n",
    "2. 计算相关系数并绘制热力图\n",
    "3. 绘制2023年全年收盘价时序图\n",
    "4. 绘制2023年4-6月K线图并进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import mplfinance as mpf\n",
    "from IPython.display import display\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# 设置中文显示\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "# 设置matplotlib内联显示\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务1：数据读取与基本分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据形状: (242, 11)\n",
      "行数: 242, 列数: 11\n",
      "\n",
      "数据类型:\n",
      "日期     datetime64[ns]\n",
      "开盘            float64\n",
      "收盘            float64\n",
      "最高            float64\n",
      "最低            float64\n",
      "成交量             int64\n",
      "成交额           float64\n",
      "振幅            float64\n",
      "涨跌幅           float64\n",
      "涨跌额           float64\n",
      "换手率           float64\n",
      "dtype: object\n",
      "\n",
      "数据前5行:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>开盘</th>\n",
       "      <th>收盘</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>成交量</th>\n",
       "      <th>成交额</th>\n",
       "      <th>振幅</th>\n",
       "      <th>涨跌幅</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>257.66</td>\n",
       "      <td>261.73</td>\n",
       "      <td>263.17</td>\n",
       "      <td>251.25</td>\n",
       "      <td>121374</td>\n",
       "      <td>3.108609e+09</td>\n",
       "      <td>4.62</td>\n",
       "      <td>1.36</td>\n",
       "      <td>3.51</td>\n",
       "      <td>1.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>261.25</td>\n",
       "      <td>260.35</td>\n",
       "      <td>261.73</td>\n",
       "      <td>257.75</td>\n",
       "      <td>72675</td>\n",
       "      <td>1.881538e+09</td>\n",
       "      <td>1.52</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-1.38</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>263.77</td>\n",
       "      <td>266.33</td>\n",
       "      <td>268.84</td>\n",
       "      <td>262.31</td>\n",
       "      <td>118936</td>\n",
       "      <td>3.143084e+09</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.30</td>\n",
       "      <td>5.98</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-06</td>\n",
       "      <td>269.16</td>\n",
       "      <td>267.45</td>\n",
       "      <td>272.45</td>\n",
       "      <td>266.35</td>\n",
       "      <td>138855</td>\n",
       "      <td>3.726793e+09</td>\n",
       "      <td>2.29</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.12</td>\n",
       "      <td>1.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-09</td>\n",
       "      <td>269.25</td>\n",
       "      <td>264.63</td>\n",
       "      <td>269.25</td>\n",
       "      <td>263.54</td>\n",
       "      <td>114169</td>\n",
       "      <td>3.020383e+09</td>\n",
       "      <td>2.13</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>-2.82</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          日期      开盘      收盘      最高      最低     成交量           成交额    振幅  \\\n",
       "0 2023-01-03  257.66  261.73  263.17  251.25  121374  3.108609e+09  4.62   \n",
       "1 2023-01-04  261.25  260.35  261.73  257.75   72675  1.881538e+09  1.52   \n",
       "2 2023-01-05  263.77  266.33  268.84  262.31  118936  3.143084e+09  2.51   \n",
       "3 2023-01-06  269.16  267.45  272.45  266.35  138855  3.726793e+09  2.29   \n",
       "4 2023-01-09  269.25  264.63  269.25  263.54  114169  3.020383e+09  2.13   \n",
       "\n",
       "    涨跌幅   涨跌额   换手率  \n",
       "0  1.36  3.51  1.04  \n",
       "1 -0.53 -1.38  0.62  \n",
       "2  2.30  5.98  1.02  \n",
       "3  0.42  1.12  1.19  \n",
       "4 -1.05 -2.82  0.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "描述性统计分析:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开盘</th>\n",
       "      <th>收盘</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>成交量</th>\n",
       "      <th>成交额</th>\n",
       "      <th>振幅</th>\n",
       "      <th>涨跌幅</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>242.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>2.420000e+02</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>242.000000</td>\n",
       "      <td>242.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>251.125950</td>\n",
       "      <td>250.679504</td>\n",
       "      <td>253.678058</td>\n",
       "      <td>247.923967</td>\n",
       "      <td>113649.466942</td>\n",
       "      <td>2.837529e+09</td>\n",
       "      <td>2.293884</td>\n",
       "      <td>-0.090289</td>\n",
       "      <td>-0.238926</td>\n",
       "      <td>0.975992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.424411</td>\n",
       "      <td>22.576906</td>\n",
       "      <td>22.745724</td>\n",
       "      <td>22.167884</td>\n",
       "      <td>49020.714708</td>\n",
       "      <td>1.283206e+09</td>\n",
       "      <td>1.027264</td>\n",
       "      <td>1.698179</td>\n",
       "      <td>4.280406</td>\n",
       "      <td>0.420603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>185.730000</td>\n",
       "      <td>187.280000</td>\n",
       "      <td>191.080000</td>\n",
       "      <td>185.250000</td>\n",
       "      <td>41380.000000</td>\n",
       "      <td>7.900910e+08</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>-5.180000</td>\n",
       "      <td>-13.430000</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>242.225000</td>\n",
       "      <td>241.042500</td>\n",
       "      <td>243.862500</td>\n",
       "      <td>239.455000</td>\n",
       "      <td>81543.000000</td>\n",
       "      <td>1.981953e+09</td>\n",
       "      <td>1.562500</td>\n",
       "      <td>-1.072500</td>\n",
       "      <td>-2.837500</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>252.865000</td>\n",
       "      <td>252.225000</td>\n",
       "      <td>255.755000</td>\n",
       "      <td>250.245000</td>\n",
       "      <td>100847.000000</td>\n",
       "      <td>2.497972e+09</td>\n",
       "      <td>2.140000</td>\n",
       "      <td>-0.180000</td>\n",
       "      <td>-0.465000</td>\n",
       "      <td>0.865000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>265.722500</td>\n",
       "      <td>265.657500</td>\n",
       "      <td>268.782500</td>\n",
       "      <td>262.787500</td>\n",
       "      <td>128399.250000</td>\n",
       "      <td>3.207182e+09</td>\n",
       "      <td>2.610000</td>\n",
       "      <td>0.770000</td>\n",
       "      <td>1.925000</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>307.120000</td>\n",
       "      <td>305.580000</td>\n",
       "      <td>307.380000</td>\n",
       "      <td>301.210000</td>\n",
       "      <td>307863.000000</td>\n",
       "      <td>8.144001e+09</td>\n",
       "      <td>6.340000</td>\n",
       "      <td>6.460000</td>\n",
       "      <td>18.530000</td>\n",
       "      <td>2.640000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               开盘          收盘          最高          最低            成交量  \\\n",
       "count  242.000000  242.000000  242.000000  242.000000     242.000000   \n",
       "mean   251.125950  250.679504  253.678058  247.923967  113649.466942   \n",
       "std     22.424411   22.576906   22.745724   22.167884   49020.714708   \n",
       "min    185.730000  187.280000  191.080000  185.250000   41380.000000   \n",
       "25%    242.225000  241.042500  243.862500  239.455000   81543.000000   \n",
       "50%    252.865000  252.225000  255.755000  250.245000  100847.000000   \n",
       "75%    265.722500  265.657500  268.782500  262.787500  128399.250000   \n",
       "max    307.120000  305.580000  307.380000  301.210000  307863.000000   \n",
       "\n",
       "                成交额          振幅         涨跌幅         涨跌额         换手率  \n",
       "count  2.420000e+02  242.000000  242.000000  242.000000  242.000000  \n",
       "mean   2.837529e+09    2.293884   -0.090289   -0.238926    0.975992  \n",
       "std    1.283206e+09    1.027264    1.698179    4.280406    0.420603  \n",
       "min    7.900910e+08    0.670000   -5.180000  -13.430000    0.360000  \n",
       "25%    1.981953e+09    1.562500   -1.072500   -2.837500    0.700000  \n",
       "50%    2.497972e+09    2.140000   -0.180000   -0.465000    0.865000  \n",
       "75%    3.207182e+09    2.610000    0.770000    1.925000    1.100000  \n",
       "max    8.144001e+09    6.340000    6.460000   18.530000    2.640000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "找到日期列: 日期\n",
      "已将日期设为索引\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    # 读取Excel文件\n",
    "    file_path = \"./比亚迪后复权历史行情数据.xlsx\"\n",
    "    df = pd.read_excel(file_path)\n",
    "    \n",
    "    # 观察数据形状\n",
    "    print(f\"数据形状: {df.shape}\\n行数: {df.shape[0]}, 列数: {df.shape[1]}\")\n",
    "    \n",
    "    # 输出数据类型\n",
    "    print(\"\\n数据类型:\")\n",
    "    print(df.dtypes)\n",
    "    \n",
    "    # 输出前5行\n",
    "    print(\"\\n数据前5行:\")\n",
    "    display(df.head())\n",
    "    \n",
    "    # 对定量变量进行描述性分析\n",
    "    print(\"\\n描述性统计分析:\")\n",
    "    numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    desc_stats = df[numeric_cols].describe()\n",
    "    display(desc_stats)\n",
    "    \n",
    "    # 检查是否有日期列并进行处理\n",
    "    date_cols = [col for col in df.columns if any(keyword in col.lower() for keyword in ['日期', '时间', 'date', 'datetime'])]\n",
    "    \n",
    "    if date_cols:\n",
    "        date_col = date_cols[0]\n",
    "        print(f\"\\n找到日期列: {date_col}\")\n",
    "        df[date_col] = pd.to_datetime(df[date_col])\n",
    "        df.set_index(date_col, inplace=True)\n",
    "        print(f\"已将{date_col}设为索引\")\n",
    "    else:\n",
    "        print(\"\\n未找到明显的日期列，使用默认索引\")\n",
    "except Exception as e:\n",
    "    print(f\"读取数据时出错: {e}\")\n",
    "    # 如果读取失败，创建一个简单的模拟数据集用于演示\n",
    "    print(\"创建模拟数据集进行演示...\")\n",
    "    dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='B')\n",
    "    np.random.seed(42)\n",
    "    data = {\n",
    "        '开盘价': 250 + np.cumsum(np.random.randn(len(dates)) * 10),\n",
    "        '收盘价': 250 + np.cumsum(np.random.randn(len(dates)) * 10),\n",
    "        '成交量': np.random.randint(1000000, 10000000, len(dates))\n",
    "    }\n",
    "    df = pd.DataFrame(index=dates)\n",
    "    df['开盘价'] = data['开盘价']\n",
    "    df['收盘价'] = data['收盘价']\n",
    "    df['最高价'] = df['开盘价'] * (1 + np.random.rand(len(df)) * 0.02)\n",
    "    df['最低价'] = df['开盘价'] * (1 - np.random.rand(len(df)) * 0.02)\n",
    "    df['成交量'] = data['成交量']\n",
    "    print(f\"模拟数据形状: {df.shape}\")\n",
    "    print(\"模拟数据前5行:\")\n",
    "    display(df.head())\n",
    "    print(\"\\n描述性统计分析:\")\n",
    "    display(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务2：相关性分析与热力图绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "相关系数矩阵:\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>开盘</th>\n",
       "      <th>收盘</th>\n",
       "      <th>最高</th>\n",
       "      <th>最低</th>\n",
       "      <th>成交量</th>\n",
       "      <th>成交额</th>\n",
       "      <th>振幅</th>\n",
       "      <th>涨跌幅</th>\n",
       "      <th>涨跌额</th>\n",
       "      <th>换手率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>开盘</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986244</td>\n",
       "      <td>0.992426</td>\n",
       "      <td>0.994495</td>\n",
       "      <td>0.050700</td>\n",
       "      <td>0.255110</td>\n",
       "      <td>0.003868</td>\n",
       "      <td>0.015060</td>\n",
       "      <td>0.006034</td>\n",
       "      <td>0.050521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>收盘</th>\n",
       "      <td>0.986244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995475</td>\n",
       "      <td>0.993486</td>\n",
       "      <td>0.077566</td>\n",
       "      <td>0.286335</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.157208</td>\n",
       "      <td>0.148145</td>\n",
       "      <td>0.077389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最高</th>\n",
       "      <td>0.992426</td>\n",
       "      <td>0.995475</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993541</td>\n",
       "      <td>0.109543</td>\n",
       "      <td>0.315608</td>\n",
       "      <td>0.079231</td>\n",
       "      <td>0.106321</td>\n",
       "      <td>0.097762</td>\n",
       "      <td>0.109359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>最低</th>\n",
       "      <td>0.994495</td>\n",
       "      <td>0.993486</td>\n",
       "      <td>0.993541</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025833</td>\n",
       "      <td>0.234943</td>\n",
       "      <td>-0.033958</td>\n",
       "      <td>0.080204</td>\n",
       "      <td>0.070559</td>\n",
       "      <td>0.025652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成交量</th>\n",
       "      <td>0.050700</td>\n",
       "      <td>0.077566</td>\n",
       "      <td>0.109543</td>\n",
       "      <td>0.025833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973013</td>\n",
       "      <td>0.739192</td>\n",
       "      <td>0.226821</td>\n",
       "      <td>0.238814</td>\n",
       "      <td>0.999979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成交额</th>\n",
       "      <td>0.255110</td>\n",
       "      <td>0.286335</td>\n",
       "      <td>0.315608</td>\n",
       "      <td>0.234943</td>\n",
       "      <td>0.973013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.712590</td>\n",
       "      <td>0.274328</td>\n",
       "      <td>0.285476</td>\n",
       "      <td>0.972973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>振幅</th>\n",
       "      <td>0.003868</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.079231</td>\n",
       "      <td>-0.033958</td>\n",
       "      <td>0.739192</td>\n",
       "      <td>0.712590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.247223</td>\n",
       "      <td>0.253508</td>\n",
       "      <td>0.739124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>涨跌幅</th>\n",
       "      <td>0.015060</td>\n",
       "      <td>0.157208</td>\n",
       "      <td>0.106321</td>\n",
       "      <td>0.080204</td>\n",
       "      <td>0.226821</td>\n",
       "      <td>0.274328</td>\n",
       "      <td>0.247223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996039</td>\n",
       "      <td>0.226915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>涨跌额</th>\n",
       "      <td>0.006034</td>\n",
       "      <td>0.148145</td>\n",
       "      <td>0.097762</td>\n",
       "      <td>0.070559</td>\n",
       "      <td>0.238814</td>\n",
       "      <td>0.285476</td>\n",
       "      <td>0.253508</td>\n",
       "      <td>0.996039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>换手率</th>\n",
       "      <td>0.050521</td>\n",
       "      <td>0.077389</td>\n",
       "      <td>0.109359</td>\n",
       "      <td>0.025652</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.739124</td>\n",
       "      <td>0.226915</td>\n",
       "      <td>0.238972</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           开盘        收盘        最高        最低       成交量       成交额        振幅  \\\n",
       "开盘   1.000000  0.986244  0.992426  0.994495  0.050700  0.255110  0.003868   \n",
       "收盘   0.986244  1.000000  0.995475  0.993486  0.077566  0.286335  0.040052   \n",
       "最高   0.992426  0.995475  1.000000  0.993541  0.109543  0.315608  0.079231   \n",
       "最低   0.994495  0.993486  0.993541  1.000000  0.025833  0.234943 -0.033958   \n",
       "成交量  0.050700  0.077566  0.109543  0.025833  1.000000  0.973013  0.739192   \n",
       "成交额  0.255110  0.286335  0.315608  0.234943  0.973013  1.000000  0.712590   \n",
       "振幅   0.003868  0.040052  0.079231 -0.033958  0.739192  0.712590  1.000000   \n",
       "涨跌幅  0.015060  0.157208  0.106321  0.080204  0.226821  0.274328  0.247223   \n",
       "涨跌额  0.006034  0.148145  0.097762  0.070559  0.238814  0.285476  0.253508   \n",
       "换手率  0.050521  0.077389  0.109359  0.025652  0.999979  0.972973  0.739124   \n",
       "\n",
       "          涨跌幅       涨跌额       换手率  \n",
       "开盘   0.015060  0.006034  0.050521  \n",
       "收盘   0.157208  0.148145  0.077389  \n",
       "最高   0.106321  0.097762  0.109359  \n",
       "最低   0.080204  0.070559  0.025652  \n",
       "成交量  0.226821  0.238814  0.999979  \n",
       "成交额  0.274328  0.285476  0.972973  \n",
       "振幅   0.247223  0.253508  0.739124  \n",
       "涨跌幅  1.000000  0.996039  0.226915  \n",
       "涨跌额  0.996039  1.000000  0.238972  \n",
       "换手率  0.226915  0.238972  1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "相关系数热力图已保存为'相关系数热力图.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    # 计算相关系数\n",
    "    corr_matrix = df.select_dtypes(include=[np.number]).corr()\n",
    "    print(\"相关系数矩阵:\")\n",
    "    display(corr_matrix)\n",
    "    \n",
    "    # 绘制相关系数热力图\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=0.5, fmt='.3f')\n",
    "    plt.title('比亚迪股票各变量相关系数热力图')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('相关系数热力图.png', dpi=300)\n",
    "    print(\"相关系数热力图已保存为'相关系数热力图.png'\")\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"相关性分析时出错: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务3：2023年全年收盘价时序图绘制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用列 '收盘' 作为收盘价\n",
      "2023年收盘价时序图已保存为'2023年收盘价时序图.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    # 确保索引是日期类型\n",
    "    if not isinstance(df.index, pd.DatetimeIndex):\n",
    "        print(\"索引不是日期类型，尝试转换...\")\n",
    "        # 假设第一列是日期\n",
    "        if len(df.columns) > 0:\n",
    "            df[df.columns[0]] = pd.to_datetime(df[df.columns[0]])\n",
    "            df.set_index(df.columns[0], inplace=True)\n",
    "    \n",
    "    # 筛选2023年数据\n",
    "    if isinstance(df.index, pd.DatetimeIndex):\n",
    "        df_2023 = df[df.index.year == 2023]\n",
    "    else:\n",
    "        df_2023 = df.copy()\n",
    "    \n",
    "    # 查找收盘价列\n",
    "    close_cols = [col for col in df_2023.columns if any(keyword in col.lower() for keyword in ['收盘', 'close'])]\n",
    "    \n",
    "    if close_cols:\n",
    "        close_col = close_cols[0]\n",
    "    else:\n",
    "        # 如果没有明显的收盘价列，使用第一个数值列\n",
    "        close_col = df_2023.select_dtypes(include=[np.number]).columns[0]\n",
    "    \n",
    "    print(f\"使用列 '{close_col}' 作为收盘价\")\n",
    "    \n",
    "    # 绘制时序图\n",
    "    plt.figure(figsize=(14, 7))\n",
    "    plt.plot(df_2023.index, df_2023[close_col], '-b', linewidth=1.5)\n",
    "    plt.title('比亚迪2023年收盘价时序图')\n",
    "    plt.xlabel('日期')\n",
    "    plt.ylabel('收盘价')\n",
    "    plt.grid(True, linestyle='--', alpha=0.7)\n",
    "    \n",
    "    # 设置日期格式\n",
    "    if isinstance(df_2023.index, pd.DatetimeIndex):\n",
    "        plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
    "        plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
    "        plt.xticks(rotation=45)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig('2023年收盘价时序图.png', dpi=300)\n",
    "    print(\"2023年收盘价时序图已保存为'2023年收盘价时序图.png'\")\n",
    "    plt.show()\n",
    "    plt.close()\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"绘制时序图时出错: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任务4：2023年4-6月K线图绘制与分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K线图数据范围: 2023-04-03 00:00:00 到 2023-06-30 00:00:00\n",
      "2023年4-6月K线图已保存为'2023年4-6月K线图.png'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 20132 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 20132 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 36842 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 26376 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 36842 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 26376 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 20132 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 36842 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 26376 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:214: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 20215 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 26684 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 25104 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 20132 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 37327 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 27604 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 20122 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 36842 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 24180 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 26376 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 32447 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\backends\\backend_agg.py:183: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAFgeAQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDyCEgAAAAAAIDlEZAAAAAAAADLIyABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB4BCQAAAAAAsDwCEgAAAAAAYHkEJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMsjIAEAAAAAAJZHQAIAAAAAACyPgAQAAAAAAFgeAQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDyCEgAAAAAAIDlEZAAAAAAAADLIyABAAAAAACWR0ACAAAAAAAsj4AEAAAAAABYHgEJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5WXEU8jv94cuG4aRsMoAAAAAAADJNM3QZZuNvg3JEFdAIkknT55MZD0AAAAAAEAnQ4cOTXUVLCPugESSHA6HJXqQ+Hw+VVZWqqSkRHa7PdXVwSBC20Ii0K6QKLQtJALtColC20KipKptmaYpr9ebtO0hzoAkGIoYhmGJgMQwDJmmaZn9RfLQtpAItCskCm0LiUC7QqLQtpAoqW5btOfk4UQmAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5RGQAAAAAAAAyyMgAQAAAAAAlkdAAgAAAAAALI+ABAAAAAAAWB4BCQAAAAAAsDwCEgAAAAAAYHkEJAAAAAAAwPIISAAAAAAAgOURkAAAAAAAAMsjIAEAAAAAAJZHQAIAAABgwPN4PNq7d688Hk+qqwJggCIgAQAAADDgeb1evfPOO/J6vamuCoABioAEAAAAAABYHgEJAAAAAACwPAISAAAAAABgeQQkAAAAAADA8ghIAAAAAACA5RGQAAAAAEgLHo9HmzZtSvpUvanaLoD0QkACAAAAIC14vV5t2bIl6VP1pmq7ANILAQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDyCEgAAAAAAIDlEZAAAAAAADCAVVRUaObMmRozZoxKSkp0ww03qLKyMqxMfn5+xL+f/vSnoTJlZWVdbl+6dGmydydlMlJdAQAAAAAA0Ht79uzRLbfcosmTJ6utrU3333+/5s+fr3379iknJ0eS9MEHH4Sts2vXLv3jP/6jysvLw5YvXrxYK1euDF13OByJ34E0QUACAAAAYGByuyVvc+BybU3g//Hjks8fuOzIllyu1NQNSKINGzaEXV+3bp1KSkr03nvvadq0aZKkkSNHhpXZtm2bSktLddZZZ4UtdzqdXcpaRY8CEp/PJ8MwElWXtOHz+cL+A/2FtoVEoF0hUWhbSATaFbrTsX3EbCPuemVd9f9kmKYkyWWzqbxwhFxLlirDHwhITMNQy/Ztkiuv/7YLy0nVcctsb9v19fVh38Ozs7OVnZ3d7br19fWSAqfVRHL06FHt2LFDv/jFL7rc9tJLL+nFF19UYWGhZs+erWXLlmno0KG93Y0BpUcBSWVlZehJsoKqqqpUVwGDFG0LiUC7QqLQtpAItCtEcvLkSUlSdXV1zC9kmTU1Os801ZabI9NmU4akbzQ1Sbk5apVk+P3KaGjUx/97UK0FBf22XVhXso9bhmGoqKhIEydOVENDQ2j58uXLtWLFiqjrmaapVatWaerUqRo/fnzEMi+88IJyc3N19dVXhy2/7rrrVFRUpMLCQh06dEj33nuvDhw4oJdffrl/dirN9SggKSkpsUwPkqqqKo0dO1Z2uz3V1cEgQttCItCukCi0LSQC7Qrdqa2tlSQVFxdH/eU75OhRSZI9M1Oy22Waks/XJrs9Q4Yhqf3X/rOLi6XCwv7bLiwnVcct0zTV0tKiAwcOdOlB0p277rpLBw8e1Pbt26OWee6553Tdddd1GV9k8eLFocvjx49XcXGxZsyYoffff1+TJk3q5Z4MHD0KSOx2uyUCkiC73c4bNxKCtoVEoF0hUWhbSATaFSIJtom42octeLshyZBhBHq6B76uGO1/kt1ml2LcV4+2C8tKdvsInr2Rl5cX9/fwZcuWafv27dq2bZtGjx4dscyePXtUWVmpJ598Mub9TZo0SZmZmaquriYgAQAAAAAA6c00TS1btkxbt27V5s2bVVRUFLXss88+qwsvvFDnn39+zPs9dOiQWltbLTNoqy3VFQAAAAAAK/J4PNq0aZM8Hk+qq4IB7s4779SLL76oJ554Qrm5uTpy5IiOHDnSpW3V19fr1Vdf1Y033tjlPj755BM9/PDD+u///m999tln2rFjh2666SZdcMEFmjp1arJ2JaXoQQIAAAAAKeD1erVlyxaVlpbK6XSmujoYwNavXy9JKisrC1u+bt06LViwIHR948aNMk1T1157bZf7yMzM1FtvvaXHHntMjY2NGj16tObOnavly5db5tQzAhIAAAAAAAaw4EDDsSxZskRLliyJeNsZZ5yhrVu39mOtBh5OsQEAAAAAAJZHQAIAAAAAACyPgAQAAAAAAFgeAQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDyCEgAAAAAAIDlEZAAAAAAAADLy0h1BQAAAABYV01Lo5p8LZKkem+dJOkv3jo1egK3D7FnqSArJ1XVA2AhBCQAAAAAUqKmpVGn77pDfpmSpMxWacK50sX7H1RrZqCMTYYOz64gJAGQcAQkAAAAAFKiydciv0wNz8yR3bBJWdLhSVJ+++0+068TrYEeJgUiIAGQWAQkAAAAAFLKbtgCAQkApBBHIQAAAAAAYHkEJAAAAAAAwPI4xQYAAADAwOX3t18wA5d9PklGh+VRuN2St/nUZUk6dlxqaT1VxpEtuVz9XWMAaYqABAAAAMDA48iWaRgy2sMNj82m1wtH6Iqjx+RsD0dMwwiEHJ253bLPvVKGGZg9J9dmU3nhCOUuWqyMDsGKaRjy7XiNkASwCAISAAAAAAOPyxUIL9p7gbhra7SpokJTH35ImfkFgTLReoB4m2WYpkyXS7LZ5JA0r7lFcrnaJxyW5PcHwhdvs0Q+AlgCAQkAAACAgcnlOhVe2NuHVzzttMBfPGw2yW5PSNUADDwM0goAAAAAACyPgAQAAAAAAFgeAQkAAAAAALA8AhIAAAAAAGB5BCQAAAAAAMDyCEgAAAAAAIDlEZAAAAAAGPAcDocuueQSORyOVFcFwABFQAIAAABgwHM6nbr00kvldDpTXRUAAxQBCQBgQPF4PNq0aZM8Hk+qqwIAAIBBhIAEADCgeL1ebdmyRV6vN9VVAQAAwCBCQAIAAAAAACyPgAQAAAAAAFgeAQkAAAAAa/L7JZ8v8p/fn+raAXGrqKjQzJkzNWbMGJWUlOiGG25QZWVlWJn8/PyIfz/96U9DZZqbm7Vs2TIVFxdr9OjR+s53vqP/+7//S/bupAwBCQAAAABrcWTLNAwZbreM2lp53W69mp0lb/t1o7ZWhtst0zAkR3bX9d1u6chR6chReT79TJteeEGeTz8LLdORo4EyQJLs2bNHt9xyi3bs2KGNGzeqra1N8+fPV2NjY6jMBx98EPb385//XIZhqLy8PFRm5cqV2rp1q5588klt375djY2Nuv766+Xz+VKxW0mXkeoKAAAAAEBSuVzy7XhN8jZLkhrcbm1a84imrVmjTJfrVDlHttTxuiS53bLPvVKGaUqSMmw22QpHKOMnP1NGh14npmEEttF5fSABNmzYEHZ93bp1Kikp0Xvvvadp06ZJkkaOHBlWZtu2bSotLdVZZ50lSXK73Xr22Wf12GOP6fLLL5ckPf7445o4caJ2796tWbNmJXw/Uo2ABAAAAID1uFxSMLvIygz8H3GalJ/f/XreZhmmKdPlkmw2OSTNa26RXC6ZwTJ+vwy3OxDAkI8gBerr6yUFTquJ5OjRo9qxY4d+8YtfhJa9//77am1t1cyZM0PLRo0apfPOO0/79+8nIOnM5/PJMIxE1SVtBLsPWaUbEZKHtoVEsFq76ri/VtnnVLFa20Jy0K7Qkc8faAdm+19nZodysdpMX9pWj95b/L7AlyibIdmjjVgQqLnP3z6eSX9sFymTquOW2d5Lqb6+Pux7eHZ2trKzI5z61WG9VatWaerUqRo/fnzEMi+88IJyc3N19dVXh5YdOXJEWVlZGjZsWFjZwsJCHTlypC+7MmD0KCCprKwMPUlWUFVVleoqYJCibSERrNKuTp48KUmqrq7W0KFDU1wba7BK20Jy0a4gSUfaAsd0X1ubZHQNG3xm4JSVj6ur1ZgR3zG/N22rJ+8tmTU1Ok9Sm88nRftu5PcrU4F6t9bW9st2O2tubta7776riy66qNsvy+g/yT5uGYahoqIiTZw4UQ0NDaHly5cv14oVK6Kud9ddd+ngwYPavn171DLPPfecrrvuOjkcjpj1ME3TEh0lpB4GJCUlJZZ4YHw+n6qqqjR27FjZ7fZUVweDCG0LiWC1dlXb/kGzuLg4ardR9A+rtS0kB+0KHeV4a6UvJHtGhuwRAhKZfqlFOru4WGc4uj/m96Vt9ei95ehRSVKG3S5F2057T4Ozi4ulwsL+2W6EdX/5y19q3rx5vB8mWKqOW6ZpqqWlRQcOHOjSgySaZcuWafv27dq2bZtGjx4dscyePXtUWVmpJ598Mmz5yJEj1dLSorq6urBeJMeOHdOUKVP6uDcDQ48CErvdbomAJMhut/PGjYSgbSERrNKugvtolf1NBzzWSATaFSTJbgu0AaP9rzOjQ7l420tv2laP3ltswduj1Vqh5XZbNyFKT7fbj+uid5L9WAfP3sjLy4v5Pdw0TS1btkxbt27V5s2bVVRUFLXss88+qwsvvFDnn39+2PJJkyYpMzNTb775pq655hpJ0pdffqlDhw7phz/8YR/3ZmBgkFYAAAAA6KkOM9b06DYgAe68805t2LBBzz//vHJzc0NjhuTl5cnpdIbK1dfX69VXX9V9993X5T5cLpcWLlyou+++WwUFBcrPz9c999yj8ePHh2a1GewISAAAluLxeLRz507NmTMn7AMDAABxcWTLNIzALDWSPDabXi8coSuOHpOz0zS/cjA2CJJj/fr1kqSysrKw5evWrdOCBQtC1zdu3CjTNHXttddGvJ8HHnhAGRkZuummm+T1ejV9+nS98MILlumlREACALAUr9erLVu2qLS0lIAEANBzLpd8O14LTOErqcHt1qY1j2jamjXKdHWY09eRHZhKGEiC2m4GA+5oyZIlWrJkSdTbHQ6HHn74YT388MP9VLOBhYAEAAAAAHrC5ZKC2UdWZuD/iNMkBksFBjQCEgAAAACW5nA4VFZWFteUp31V09KoJl+LJKneWydJ+ou3To2eU2WG2LNUkJWT8LoACEdAAgAAAMDSnE6nysvLE76dmpZGnb7rDvkVmJ0ks1WacK508f4H1Zp5qpxNhg7PriAkAZKMgAQAAAAAkqDJ1yK/TA3PzJHdsElZ0uFJUscTc3ymXydaA71MCkRAAiSTLdUVAADACjwejzZt2iSPxxO7MABgULMbtm7/AKQGrz4AAJIgOHuO1+tNdVUAAAAQAQEJAAAAAACwPMYgAQAAAIAk8pn+Xt0GILEISAAAAAAgCYbYs2SToROtjZLaZ7Gpkg6OVZdZbIbYs1JUS8C6CEgAAEBa8Xg82rlzp+bMmSOn05nq6gBAvynIytHh2RVq8rVIkurr6rR264N6asFK5Q0bFio3xJ7FFL9AChCQAACAtBIc0La0tJSABMCgU5CVE5q+t7Z93O5RjmHKd+Z3sxaAZGCQVgAAAAAAYHkEJAAAxMnj8WjTpk3yeDyprgoAAAD6GQEJAABxCp764fV6U10VAAAA9DMCEgAAAABIAYfDobKyMjkcjlRXBYAYpBUAMNi53ZK3Ofy6JB07LrW0Bi47siWXK/l1AwBYmtPpVHl5eaqrAaAdAQkAYPByu2Wfe6UM0wwtyrXZVF44QrmLFivD75ckmYYh347XCEn6EVP1AgCAgYZTbAAAg5e3WYZpynS5ZObny8zPl8Pl0rzmFjmCy1yuQIDSsZcJ+ozxWgAAwEBDQAIAGPxsNsluj/xnS/+3QmbPAQAASLz0/1QIAIDF0RsDAAAg8QhIAAAAAKNOD2cAACAASURBVACA5RGQAAAAAOg3nBYIYKBiFhsAABKgpqVRTb6W0PV6b50k6S/eOjW2f2cYYs9SQVZOKqoHAAkTPC2wtLSUWawADCgEJAAA9LOalkadvusO+XVqeuHMVmnCudLF+x9Ua2ZgmU2GDs+uGDQhScdQqMeBkNt9aiYhtzvw/9hxqaU1cNmRzTTMAAAgoQhIAACIore9QJp8LfLL1PDMHNmN9rNZs6TDk6T89jI+068TrYH7L1Cn0KBjWBC8LqV1YNA5FOpRIOR2yz73ysB0y5JybTaVF45Q7qLFyvD7JUmmYci347WY++zxeLRz507NmTOHX67RBe0DANAdAhIAACLoj14gdsN2KiCJV6ewQOpbYJAsXUKhngRC3mYZpinT5ZJsNjkkzWtukVyuwKPv98sIhkYxdpeu/egO7QOJ4HA4VFZWJofDkeqqAOgjAhIAACLocy+Q3uoUFkjqU2CQbL0KhYJsNslu798KAUCCOZ1OlZeXp7oaAPoBAQkAAN3o0xf+viAsAAAASCoCEgAAEsRn+nt1GwAAAJKPgAQAgH42xJ4lmwydaG0MLctslSZUSQfHKmz8kiH2rBTVEgAAAB0RkAAA0M8KsnJ0eHZF+Aw4dXVau/VBPbVgpfKGDZMUY9pbAACAOFVUVGjLli2qrKyUw+HQlClTtHr1apWUlISV+/DDD7V69Wq9/fbbMk1T48aN0/r16zVmzBhJUllZmd5+++2wda655hqtX78+afuSSgQkAAAkQEFWTtjArbXewP9RjmHKd+ZHWQsAAKDn9uzZo1tuuUWTJ09WW1ub7r//fs2fP1/79u1TTk7g88gnn3yiq666SgsXLtTKlSuVl5enDz/8sMsMTIsXL9bKlStD1600QxMBCQAAAAAAA9iGDRvCrq9bt04lJSV67733NG3aNEnSfffdpzlz5ujee+8NlTvrrLO63JfT6dTIkSMTWt90lYJh+QEAAAAAQKLU19dLkvLzA71W/X6/du7cqbFjx+raa69VSUmJZs+era1bt3ZZ96WXXlJxcbEuvfRS3XPPPTp58mRS655KPepB4vP5ZBhGouqSNnw+X9h/oL/QtpAIVmhXNS2NavIHxvOob3JLkr5oqlF91ql9HmKLMJ6H39f+Rme2/0USWO7z+6QOj6HP74tzzUDZWI9/x+ep27J9qHMkHo9Hb7zxhmbNmiWn09lt2e7qHLNsjMer28cq5j7Hv79xP84R9OWxQvxSeczqS/tA/HryOPfp2NHNdq2A413ypKptmWbgFVBfXx/2PTw7O1vZ2dndrrdq1SpNnTpV48ePlyQdO3ZMDQ0NWrt2rVatWqXVq1dr165duvHGG7V58+ZQL5PrrrtORUVFKiws1KFDh3TvvffqwIEDevnllxO4p+mjRwFJZWVl6EmygqqqqlRXAYMUbQuJMFjbldvn1eWfPy6z/WNyZqs04Vxp6h8eCs0GI0mGDO0e83dy2U+dJ5tZU6PzJLX5fFK09y+/X5mSPq6uVmttbWjxkbbAryW+tjbJiNzhMjhV78fV1WrMGNrtfgR/famurtbQodHL9qXO0ba7detWfeUrX+l2u92Jp23Fery6e6xi7nMP91eK/ThHW7evjxXil4pjVl/aB+LXk8e5L8eOaAbr+2FnHO+SL9ltyzAMFRUVaeLEiWpoaAgtX758uVasWBF1vbvuuksHDx7U9u3bQ8v8/sBr6aqrrtKtt94qSTr//PO1f/9+rV+/PhSQLF68OLTO+PHjVVxcrBkzZuj999/XpEmT+nX/0lGPApKSkhLL9CCpqqrS2LFjZbfbU10dDCK0LSTCYG9XX3hrZX5uanhmjmyGTcqS/m+SNKxDGb/p14nWRp1+9pk6w9FhANSjRyVJGXa7FO2xaf816OziYqmwMLQ4x1srfSHZMzJkjxKQyPRLLYF1w7YbQW37F/vi4uJQd9eI+lDnPm034mbib1sxH6/uHqtY+5yk/e3LuohfKo9ZPMfJ0ZPHOXjsMOy2iAGJYUryx3ecHezvh51xvEueVLUt0zTV0tKiAwcOdOlBEs2yZcu0fft2bdu2TaNHjw4tHz58uDIyMjRu3Liw8uecc4727dsX9f4mTZqkzMxMVVdXE5B0ZrfbLRGQBNntdkscXJF8tC0kwmBtV3ZbYJ/shi1qUOHrUDbsMbAFLxvtf5EYp7bTYd3gdmOvGWG7EQRvj/k89aHOfdpujPuIuX8xHq9uH6uY+5yc/e2PxwrxS8Xj3Jfn2OPxaOfOnZozZw6nJMTQk8d5aKZTNhk60dooqb2XYJV0cKxCvQRtMjQ00xn3c2aV1zDHu+RL9uMVPHsjLy8v5vdw0zS1bNkybd26VZs3b1ZRUVHY7VlZWZo8ebIqKyvDlldXV4em+I3k0KFDam1ttcygrcxiAwAAAMTg9Xq1ZcsWlZaWEpD0o4KsHB2eXaEmX/s4U3V1Wrv1QT21YKXyhgX6Cg6xRxhjCkCYO++8Uxs2bNDzzz+v3NxcHTlyRFIgXAkes26//XYtXbpUl112mUpLS7Vr1y699tpr2rx5s6TANMAvvfSS5syZo+HDh+uDDz7QPffcowsuuEBTp05N2b4lEwEJAABJ4HA4VFZWJofDEbswAFhIQVaOChQIQDwKHCv/athXCKKAHli/fr0kqaysLGz5unXrtGDBgtBtFRUV+vGPf6wVK1Zo7NixeuaZZ3TppZdKkjIzM/XWW2/pscceU2Njo0aPHq25c+dq+fLllulpREACAEASOJ1OlZeXp7oa6a19ALm4lwNIGzUtjad6gXjrJEl/8dap0RO4Pd5eIBwrgd6pjTGIedDChQu1cOHCiLedccYZEaf9tRICEgDoJ5yfDvSSI1umYchwu6MWMQ1DckQflA5AV8l6X6ppadTpu+6Qv9NsYxfvfzBsHJHDsys4VaaPYgVREqckAX1BQAIA/YTz04Fecrnk2/Ga5G2OXsaRLblcyasTMAgk632pydcivwKzjdnbZxs7PEkKzo3ia59prMnXEjqVBj0XTxAlEUYBfUFAAgAAUs/lksg/ACCqWEGURBgF9BUBCQAAAIBeG2LPimuq3iH2rBTWcvDobtp7AH1DQAIAAACg15iqN7l8ZvSBq7u7rT8x7hoGKwISAAAGE7c7fCyP4MCnx45LLa2By4znAaCfdZyqt9YbWDbKMUz5zvxu1kJPxNNTR0pObx3GXcNgRUACAMBg4XbLPvdKGaYZWpRrs6m8cIRyFy1WRvt0uaZhBAZFJSQBgAGjc0+dZq9Xe33/pUunlyrb4QiVo7cO0HsEJACApKNrboJ4m2WYpkyXS7IFzk93SJrX3CK5XIF5D/z+wHS63mYGRZXCe9zQ2wYd0RsLaahjTx05peL534p/5VjHO4k2DcsjIAEAJB1dcxPMZpPs9lTXImlqWhpPjX3grZMk/cVbp0ZP4Paov6Z26nFDbxuE0BuLIHuwieN4Jw3uNg3Eg4AEAAAMWDUtjTp91x3yB/rHBM7JP1e6eP+DYbNnHJ5d0TUk6dTjht42CKE3FkH2YBPreCcN+jYNxIOABAAADFhNvhb5ZSo/Y4hshiFlSp9fIOW13+43TdW2NanJ13KqW3pnFutxgx6gbWCwoU0D3SIgAYC+YPwCIKWCszrUtjVJijyrQzJmdAAAAAMfAQkA9BbjFwAp13lWh/q6Oq3d+qCeWrBSecOGSWJGByDZHA6HysrK5OgwswoADAQEJADQW4xfAKSFjrM61HoDy0Y5hinfmZ/CWgHW5XQ6VV5enupqAECPEZAAQF9xPi8AYBDo9YxQADBIEJAAAJCOOky72KPbAKAX+jQjFAAMEgQkAACkE0e2TMMInJ7VzmOz6fXCEbri6DE5O4xvI0d2qmoZlc+MHN5EWw4kUsceERK9IroTnBFqeGaO7IZNypIOT5KCJ6r5TL9OtDZ2PyMUAAxwBCQAgAGhuy/Yg+rLt8sVGNg3ODuSpAa3W5vWPKJpa9YoMzjgbxJmSPJ4PNq7d6/OPPNM5ebmdls2OJvMidZGScwmg9Tr3CNColdEPOyGLRCQAIAFEZAAANJaPF+8pUH25dvlCh/YN6t9R0ecJuUnb+BRr9erd955R/PmzYsZkDCbDNJNlx4REr0igDTg8Xi0c+dOzZkzR06nM9XVAcIQkAAA0lo8X7wlvnynA2aTQTqyWo+Ivg60ymlySDSv16stW7aotLSUgARph4AEAJD2+OINALH1ZaBVTpMDAAISAAAAYFDoy0CrnCYHAAQkAAAAwKDS29OK6K2HaPo0I5TbHTbwuIKztB07LrW0Bi4nYeBxIB4EJACAwc/fzbnz3d0mC82eAwAY/HrxftinGaHcbtnnXinDPLVurs2m8sIRyl20WBkdpq737XiNkAQpR0ACABi8HNkyDUNG8NcqSR6bTa8XjtAVR4/J2eGDmRzZYat2Ph9f4px8AMAA1en9MNJ7oRT5/TB46lZ+xhDZDCOwMFP6/AIpr72M3zRV29bU9fQtb7MM05Tpckm2QK8mh6R5zS2SyxWIXPz+QL28zeEzuAEpQEACAEiKWDMrSAk4v93lCvwi1aFrb4PbrU1rHtG0NWuUGfylKkLX3s7n40uckw8AGKA6vR9GfC+UIr4fBn8wqG1rCi3r8Q8GNptkt/d5N5giGIlGQAIASLh4ZlaQos+u0CcuV/gvUlntGxxxmpTf/Xn1Hc/HlzgnHwAwgHV8P+zhe2G6/GDAFMFINAISAEDCxZpZQep+dgUAAJA6/GAAq+j58NYAAPRScGaFaH8ABj+Px6O9e/fK4/HELgwAQBLxaRQAYCkOh0NlZWVyOByprgoSgOc3/Xm9Xr3zzjvyer2prgoAAGE4xQYAYClOp1Pl5eWprkba6jiYbm1zYLaDvzS75fUEBtdL90FpeX4BAEBvEZAAADDYdJiysSe3RRtMd+ofHgqbpaDfB9LFoBcreJPSP3wDgHRWUVGhLVu2qLKyUg6HQ1OmTNHq1atVUlISVu7DDz/U6tWr9fbbb8s0TY0bN07r16/XmDFjJEnNzc2655579Jvf/EZer1fTp0/XmjVrNHr06FTsVtIRkACwvI4f3CMZzB/amS5vkHFkyzQMGW53aJHHZtPrhSN0xdFjcraHI6ZhBKZy7KTzYLpmlvTZhDYNy8iQIQbSRe/EE7xJ3YRvbnfYVN0er1c797ytOZdNkzN4KlWEqUkHC47TAOKxZ88e3XLLLZo8ebLa2tp0//33a/78+dq3b59ycgLH1U8++URXXXWVFi5cqJUrVyovL08ffvhh2GmpK1eu1Ouvv64nn3xSBQUFuvvuu3X99ddr9+7dsvfDVM3pjoAEgKV1/uAeyWD+xZzp8gaGuMfVcLnk2/Fa2JfJBrdbm9Y8omlr1igz+AUyxpfJ4KC5piS1Xzb6vhuwqFjBm9RN+OZ2yz73ShnmqWN0hs0mW+EIZfzkZ8roEPr5drw2OEKSToGQ1+0OHKfHnSdnnK9hANazYcOGsOvr1q1TSUmJ3nvvPU2bNk2SdN9992nOnDm69957Q+XOOuus0GW3261nn31Wjz32mC6//HJJ0uOPP66JEydq9+7dmjVrVsL3I9V6FJD4fD4ZxuD/iOTz+cL+A/2FtpV+TrZ6Qh/cbRFmUfG3f2g/2eqRy97py6nf134QNdv/Ogss8/l9UgKf8760q47rJrJd+vyB+472SKnDcp+/+7okq87ptO2srCx94xvfCKtDVLm5gb92PnugXfsK8uXL7zAVY4T76fI8Bb+UmqZMw4j7OepYz1Q8T3FLk9fwYBdsVzbDFjjOmmYgeJMhtX+ujNq2mpqUYZoyXS7JFijrkDSvuVly5QXW85sy3G75mprC235fjjsx28apteNpHx6PR2+88YZmzZrVfRjtrlfWVf8vLBDKtdlUXjhCuYsWhwVCLdu3Sa68sNVj7fOgew13wues+PX1+Y17/X5+LaWqXaaqbZntx4L6+vqw7+HZ2dnKzu7aE7Sj+vp6SVJ++3u/3+/Xzp07dfvtt+vaa6/Vn/70JxUVFemf//mfQ58x3n//fbW2tmrmzJmh+xk1apTOO+887d+/n4Cks8rKytCTZAVVVVWprgIGKdpW+jjSdlKSZPr8ivQTuWkGPox+XF2txoyhYbdl1tToPEltPt+pL5Id+f3KbF+3tba2n2veVW/a1cmTgf2vrq7W0KFDY5TuveDj7Gtrk6JM5+vr5rHuqLm5WZdccok+//xzHT16tP8r241Ubru3evIcR3ueQh8M43yOerrdVEm31/BgFatdSdHbVug5Mv2SGWXyRTPy89SX407MtiH1qH2cPHlSW7du1Ve+8pVuXw+ZNTU6zzTVlpsj0xaoc4akbzQ1Sbk5apVk+P3KaGjUx/97UK0FBWHrx9rnwfYajobPWbH19fmNd/1EvJb6Uu++SnbbMgxDRUVFmjhxohoaGkLLly9frhUrVkRdzzRNrVq1SlOnTtX48eMlSceOHVNDQ4PWrl2rVatWafXq1dq1a5duvPFGbd68WdOmTdORI0eUlZWlYcOGhd1fYWGhjhw5kpidTDM9CkhKSkos04OkqqpKY8eOtcR5Vkgeq7WtuH8xS6Ecb630hWTPyJA90gdo0y+1SGcXF+sMR374be1fkDPsdinS89n+4f/s4mKpsLC/q95hM71vV7XtH0SKi4tDvzAkQszHWer+se7kggsuSEAt45PKbfdGT57jLs+Tacrn8wXalWH06DlKVtvqkzR5DQ92MduVFL1txXqOpKjPU5+OO33YbiRxvx7at2vPzOzVdvv0ntbbOqcRq33O6ou+Pr89bdNJfy31s1S1LdM01dLSogMHDnTpQdKdu+66SwcPHtT27dtDy/ztPdCuuuoq3XrrrZKk888/X/v379f69etDp+FEq4cVcgCphwGJ3W63zAMjBfaXgysSwSptq7W1VVu3btX06dOV26Hbczqx2wLPg6GIHUhCy+y2CM+Zzd6hVPS17bZuPhT0o960q2D5RLfJWI+zFOOxRq/15Dnu/DyZwfd8wwh77uJ5jpLVtvokzV7Dg1WsdhW8LVg2rL0EnyO/KSnKDEx+89R2Oqzbp+NOzLZxau142kfcr4c+brdP72m9rXMaGoh1Tra+Pr/JatO93m6CJHu7wbM38vLy4v4evmzZMm3fvl3btm0Lm3lm+PDhysjI0Lhx48LKn3POOdq3b58kaeTIkWppaVFdXV1YL5Jjx45pypQpfd2dASFKnA4AAACkWIeZmYzaWhm1tfK63Xo1O0ve4DK3O+rMTAAGtpqWRn3hqQ39/cVbJ0n6i7cutKympTHFtUwPpmnqrrvu0pYtW7Rp0yYVFRWF3Z6VlaXJkyersrIybHl1dXVoit9JkyYpMzNTb775Zuj2L7/8UocOHbJMQMIsNhF4PB7t3btXZ555Ztr+6g0AQKIExykwg5dNf2iaXyCp+mlmJgADT6SZBoPThF+8/8HQNOGDebbBnrjzzju1YcMGPf/888rNzQ2NGZKXlxc61f3222/X0qVLddlll6m0tFS7du3Sa6+9ps2bN0uSXC6XFi5cqLvvvlsFBQXKz8/XPffco/Hjx4dmtRnsCEgi8Hq9eueddzRv3jwCEgCAZQyxZ8kmQydaA7/GZbZKE6qkg2MV9kF0iD0rhbWE5bhcUsfsI6u9MY44TYpjDILugr20Df383dSru9uAJIl7+vk+6DxFuCQpSzo8SQq+8qNOEW5B69evlySVlZWFLV+3bp0WLFgQuq2iokI//vGPtWLFCo0dO1bPPPOMLr300lD5Bx54QBkZGbrpppvk9Xo1ffp0vfDCC5Y5bY2ABAAASJIKsnJ0eHaFmnwtkqTamhr9bOtDevL65cpvny1jiD3L8r/SoXei9UzqeFt/6hz4SQMg9OtwSlGQx2bT64UjdMXRY3J2mOaXU4qQSk6nU+Xl5UnZlt2wRR9oGSG1cc62tnDhQi1cuDDq7Q6HQw8//LAefvjh/qragEJAAgAAQgqyckK/wjmyAzMLjMp26TTnwJjFAsnh8Xi0c+dOzZkzJ+YsZfH0TJL6P6joHPhJUn1dndZufVBPLVipvPYBCNMq9OOUIgBIKQISAAAA9IjX69WWLVtUWloaMyCJp2eSlJigomPgJ0m13sD/UY5hyk/X0K+PpxQBAHqPgAQA0kBPfo0FgIGGnkkAMLi1tbXJ34Mxkmw2mzIy0i+OSL8aAYAF9eTXWAAAACCdzJo1Sy6XS6ZpdlvOMAyZpqmmpia98cYbSapd/AhIAAAAAABAr5mmqU2bNsVdfubMmQmsTe8xHDAAAAAAAOg1wzBiF+pD+WQhIAEAAAAQxuFwqKysTA6HI9VVAYCk4RQbAADQ7/hyBQxsTqdT5eXlqa4GEoRjNBAZAQkAAOh3fLkCgPTFMRqIjIAEAAAAAAD0WkFBgebOnduj8umIgAQAAACIxO/v3W0p5jMj1y3acgDoqwsvvFCfffZZ3OXPPvvsBNam9whIAAAYxDjPHOgFR7ZMw5DhdocWeWw2vV44QlccPSZnezhiGobkyE5VLbsYYs+STYZOtDZKkjJbpQlV0sGxUmtmoIxNhobYs1JYSwCD0W9/+1s9++yzMk0zrvI33XSTVq1aleBa9RwBCQAAgxjnmQO94HLJt+M1ydscWtTgdmvTmkc0bc0aZbpcgYWObCl4OQ0UZOXo8OwKNflaJEn1dXVau/VBPbVgpfKGDZMUCFEKsnJSWU0Ag5BpmhozZkyPyqcjAhIAABCRw+HQJZdcQu8TWJPLJXXMPrLau2CMOE3Kz+9+Xbc7LFxRsCfKseNSS2vgcoLClYKsHBUoEIDUegPLRjmGKd8Zo84A0AeGYSS0fLIQkAAAgIicTqcuvfRSOZ3OVFcF6BdJOeXM7ZZ97pUyOvw6mmuzqbxwhHIXLVZGh9NzfDteS6seKABgdQQkQQ2NUmPgfE3V1gT+Hz8u+doHs8rJkXLpjggAADBQJeWUM2+zDNOU6XJJNpskySFpXnOL5HLJlCS/PzC+ibc5vJcKACClCEja2Z5/XrZ/f1KSlJWRoXPOOlNZS29RRlubJMl/y83y/+13I6/cuRtlZ2l2fiqAfhZtJoM0nuEAAJBgNptkt6e6FgCQFKZp6uGHH467bLoiIGnnX7BA/nnzJEkttTX6qKJCLev/XW357fMz50TpPRKhG2VndKEEBqlOsxwMhBkOAAAAkm6ATpmN+K1Zs0YnT56Mu/zMmTMTWJveIyAJyu1wCo090B1Sp50W+OtOhG6UYehCCQxenWY5GAgzHACIgF5gAJAYA3TKbPTclClTUl2FfkFA0l/oRglYU8dZDnoywwGA1KMXWI/UtDSemj7WWydJ+ou3To2eU2XimUKW2ZEACxmgU2bDughIAACANdELLG41LY06fdcd8geGGFVmqzThXOni/Q+qNfNUOZsMHZ5d0W1IMlBnR0rKDDj9bCDWGYNQX6bMBpKMgAQAAFgXvcDi0uRrkV+mhmfmyG7YpCzp8CSp46PkM/060RroZVKgwTfzX1JmwOlnA7HOAJBKBCQAAACIi92wBQISAOglejYhnRGQAECqdJwiPDh42bHjUkvrqTJ07weShg/t6FfM2gFERM8mpDMCEgBIhU5ThOfabCovHKHcRYuV0eGDM9OEA8kzED+0ezwe7dy5U3PmzBlwY3oMWszaAQADFgEJAKRCpynCHZLmNbdILlf7EIhimnAAMXm9Xm3ZskWlpaU9DkgIVxKEWTsAYMAiIAGAVGKKcAAp0pdwBTEwawcADEiMsgUAAAAAACyPgAR94vF4tGnTJnk8nlRXBQihXQIA0gkDAAPAwEBAgj4Jds/1er2prgoQQrsEAKST4ADAnMoEAOmNgAQAAAAAkJZ8pr/bPwRUVFRo5syZGjNmjEpKSnTDDTeosrIyrMytt96q/Pz8sL85c+aElSkrK+tSZunSpcnclZRikFYAsJialkY1+VokSfXeOknSX7x1auxwRtIQe5YKsnJSUT0AAAANsWfJJkMnWhtDyzJbpQlV0sGxUmv72Mc2GRpiz0pRLdPHnj17dMstt2jy5Mlqa2vT/fffr/nz52vfvn3KyTn1mW7WrFlat25d6HpWVtfHbvHixVq5cmXoupVODyQgAYB+MhDOMa9padTpu+6Qv30y4cxWacK50sX7Hwx90JACHzYOz64gJAEAAClRkJWjw7MrQj/qSFJ9XZ3Wbn1QTy1YqbxhwyTxo07Qhg0bwq6vW7dOJSUleu+99zRt2rTQ8uzsbI0cObLb+3I6nTHLDFY9Ckh8Pp8Mw0hUXdKGz+cL/Q9ejsrva38Qzfa/zgLLfH6fFOu+BqAePVYIe7ysIFXtoyfb9fkDt3f/Cg6Ui3VfWVlZ+sY3vhFWh6hiHjtObT3W8aMn7epkq0d+mRqemSObYZOypP+bJA3rWDXTrxOtjTrZ6pHL3j9hT6zHWerZY43ksNoxayDqy3G2P4+VUvyv4b62K6t99rDa/vYFx6zBx2V3hH0WcWQGntvCzKHKz8oLLU/0c56qtmWagSNrfX192Pfw7OxsZWdnd7tufX29JCm/09Tiv//971VSUiKXy6Vp06bp7rvv1ogRI8LKvPTSS3rxxRdVWFio2bNna9myZRo6dGh/7FLa61FAUllZGXqSBrOTJ09Kkv785z/rxIkT3ZbNrKnReZLafD4p0mPj9ytT0sfV1Wqtre3/yqZY8LGqrq62zIumP1RVVaW6CkmRqvbRk+0eaQuU9bW1SUbXYZmC57Z+XF2txoz+24eYxw4p7uNHc3Oz3n33XTU3N8d8swzur+nzS1HybjMB+xzrcZYS91ij76xyzBqIenqcdfu88pqtkqSmhkC39Xc++pOG5J769dVhZHYJR4Ov4da2VvmiHDyCPdPifQ33tl1Z7bOH1fa3P3DMGryam5t1ySWX6PPPP9fRo0eTvv1kty3DMFRUVKSJEyeqoaEhtHz58uVayIZ31QAAIABJREFUsWJF1PVM09SqVas0depUjR8/PrR89uzZmjdvnsaMGaNPP/1UDzzwgMrLy7V79+7QZ8jrrrtORUVFKiws1KFDh3TvvffqwIEDevnllxO3o2mkRwFJSUmJJXqQHD9+XJJ01lln6bTTTuu+cPsLM8Nul+z2rre3p4xnFxdLhYX9Ws90UNv+pa24uLhLOomufD6fqqqqNHbsWNkjtZdBoKalUU3+QFdIX13gQDvkjNOUM8wVuGxLfDfInrTLHG+t9IVkz8iQPdIXd9MvtQRew2c4+rGNxzp2SHEfP44fP65f/vKXKisri3nMirm/UkL2OVXbRd9Y4Zg10PXkeFfT0qiL3ryryyl2zx37D7XWnSpnk6HPZzwSdqwe0dIo2xeG6vzeU+t2GgcguO7EkvO6Pc73tV1Z7bOH1fa3LzhmWcMFF1yQ9G2mqm2ZpqmWlhYdOHCgSw+S7tx11106ePCgtm/fHrZ8/vz5ocvjx4/X5MmTdcEFF2jHjh26+uqrJQXGH+lYpri4WDNmzND777+vSZMm9cdupbUeBSR2u90SAUmw0dvt9tgvAFvwdkORf44NLLPbuvkSNID16LFCyGB9vGpaGjUmwofvqX94KGwgrUSPbdGTdmlvfw13/woOlOvX5yzmsePU1mMdP/pzf6XE7HOqtov+MViPWYNBT17/zfKFTrGzt59id3iS1PErt+//s3f34VHVd/7/XzOTTCYkJCRAIFKMNaRITKX+qlYX8SYR1BVBKa0IisWvV3HBFtdCkBX2stpLa2RTvWgQdaF2m9Vag3uVu1TxBtuKCtgFBalLqFWUEm6TkJDJhJnz+yPJkIHcze2ZyXk+risX5MznM/Oecz7nTM57PjftQ+xa5A14vqGpGQFzAXQ1D4AU3FwAobYrq/3tYbX3GwnsK0RLrNtWx+iNjIyMPt+Hl5aWqrq6Whs3btSIESN6LDt8+HCNHDlS+/bt67bM2LFjlZycrH379pEgAYBgnPR6evzju+MP75Nej7LFZFqJrrm5WZs2bdKECROUmppqdjgA+shhs3ffk6sH2c40/7X7eFtHEuW6BikrlV4NAGA2wzBUWlqqDRs2aN26dcrLy+u1zrFjx/TVV19p+PDh3ZbZs2ePWltbLTNpa/CfjgDQi44/vrv6Qf/hdru1fv16ud1us0MBEGOJsGoXAFjJggUL9Lvf/U7PP/+80tPTVVtbq9raWjU3N0uSGhsbtXTpUm3dulVffPGF/vznP2v69OkaPHiwf5GBzz77TGVlZfrf//1fffHFF3r99dc1e/ZsXXTRRbr88svNfHsxQw8SIEHwbT0iqWNC1GAfA9BJfb3kbum5jCtFysyMTTwxlJqaqsmTJ5sdBgCg3erVqyVJkyZNCtheUVGhGTNmyOFw6JNPPtFvf/tb1dfXa9iwYRo/frxWr17tnwA6OTlZ77zzjlauXKmmpiaNGDFCEydO1KJFiywzbI0ECZAgOr6tHz9+PAkShGyAwym7bDra2rZ6RU8TLQ5wOE2KEkgA9fVyTLxBtl5W9zNsNnlf/0O/TJIAAOLH8V5WTE1NTdWaNWt6LPO1r31NGzZsiGRYCYcECQBYSLYzLeITLQKW5G6RzTBkZGZK9m6GD/p8snX0MiE/AgBA3CNBAgAWw0SLQATZ7f1ylToAAKyIBAkAmMnXw3wfPT3Wee6D48fa/j1yRPJ2qtNP5z4AAAAAooEECQCYwZUiw2Zr634vqdlu12s5Q3X9ocNK7ZQYMWy2tkRHZ2fMfZBpt2tyzlBl/uBuJZ1Rt7e5D1iJAgDiG9dpAIgdEiQAYIbMzLbkRXsvkMb6eq1d9qTGLVum5M4Jja56gZwx94FLhm46eVJJmRkyZGsr08e5D1iJAgDiG9dpAIgdEiRdcLlc+s53vkOmvjudu/a3f/utw0ckT+vpMnTtB3qXmXk6eeFsX0Jm6BApq49zgfjnPjAkw2j/vy0KgQKIJ8c8TacnWnbXSZL+4a5TU/PpMj1NtMwy3wAAdI0ESRdSU1N1xRVXJNRSqs3Nzdq0aZMmTJgQ3bjP6Nqf3t61P33WXUF37bei5uZmvffeezr33HOVnp5udjgAgARzzNOkc954QD61fQ4nt0oXjpYu3fr4WUt1H7iuPCBJwjLfAAD0jARJP+F2u7V+/XqNHz8+ugmSs7r2S1NaPFJmZvufamJZwx643W598MEHmjJlCgkSAEDQTno98snQ4OQ0OWx2ySkdGCt17nfmNXw62trWy6RjxSqJZb4BAOgNCRKEhmUNAQAwjcNmb0uQBIllvgEA6F7wn6wAEAPNzc1au3atmpubey8MAAAAAGEiQQIgLnUMG3O73WaHAgCIIyx7CwCIFobYAAAAIGGw7C0AIFroQQIAcYBvRAEAAABz0YMEAOIA34gCAAAA5qIHCZgMEwAAAABgefQgiRSfL7jtJjvmadJJr0eS1FBXp/Xr12vUpd9Uhgb5ywxwOJXtTDMrRFhRfb3kbjn9f0k6fETytLb935UiZWZ2WTWgTbvrJEn/cNepqT3vR3uOD16j+2tiT48BAAAA0UaCJFyuFBk2m2ztN3PNdrteyxmq6w8dVmp7csSw2dpu7OLEMU+TznnjAflkSJKSW6ULR0uXbn1crcmny9ll04HryrmpNEnnG37JAjf99fVyTLxBNqOtXabb7ZqcM1Tps+5SUqdzyfv6H85KkvSlTdOezTXA4ZRdNh1tbZLUfoxqpN2jdNZ1Z4DDaVKUAAAAsDISJOHKzGy7YWv/1ruxvl5rlz2pccuWKbnjJq6Hb707a25u1qZNmzRhwgSlpqZGLeSTXo98MjQ4OU0Om11ySgfGSlmdyngNn462tt2gZ4sbylg784ZfssBNv7tFNsOQkZkp2e1ySZrS4pEyM9v2gs/Xloh0t0hnnE69tWnas/mynWk6cF15QM+1pzY8rhdmLFbGIHquAQAAwHwkSCIhM/P0DZuz/c516BApK6vbKl1xu91av369xo8fH9UESQeHzd52M4m4c9YNv2Sdm367XXI4QqpKm45v2c40f1s97m7blusapKzU4K6VAAAAQDRwJwHEsY4b/u5+AACJj8nSAQCID/QgAaKp86SjknT8WNu/R45I3vYJKfs4BAsA0D/FugcpAADoGgkSIFrOmHRUkjLbJx7N/MHdvU48CgAAAACIHRIkQLScMemoJLlk6KaTJ5WUmSFDth4nHgV65V9G3Gj7v9cryXbGY5HV23LKEhOtAgAAIDGRIDFZbzcbPd5odB6+0b7MsA4fkTytp8swfMN8AZOOGpJhtP9uMzMqnMFrdJ1Q6G67qfqwvLgU+SXGWSIciCA+wwEAiDskSEzUl5uNbm80zhi+kd4+dCN91l3+oRtSFIdv9PTtdJS+uUYQzpz7pCv84S2pLQlpl01HW5sktZ+HNdLuUQo4Dwc4nCZGeYYzlhevP35Ma8vLdXnZE0rOyj5djmMMxCezP8MBAECXSJCY6KylXINZxvWM4RsuSVNaPFJmpvwzXkRj+IZJ31wjCF3MfdIV/vBuk+1M04Hryk/35Kqr01MbHtcLMxYrY9AgSXE6ZKTz8uKO9hWNhgxp+4mSvuwrKU73FxBPzPoMBwAAPSJBEgfCWrI1YPhGDJzxzXVjfb3WLntS45YtU3LnG22+uTZPF3OfnIU/vANkO9P8Scjj7rZtua5BykrN6qGWNbGvgAiK9Wc4AADoEQkSBK/zN9fO9jEIQ4dIWda4QWpubtamTZs0YcKE+F6OkT+8AQA9cLlcmjRpklwul9mhAAAQF0iQAL3oPJGu1DasYP369Rp16TeVoTgegoGQcMMAwCpSU1M1efJks8MAACBukCBJdGFMltrT6hxxuXKHCc6cSFcKcjJdJBxuGAAAAABrCnHiC5iu02SptuPH5a6v1+9TnHK3/247fly2+vouJ0vtvGrHIc8JHW86oXN2tv17yNP2c7S1Kf5W7jBB54l0c5wDleMcqKy0gTowtu3fHOdADU5Ok09GQC8TAAAAAIiV8vJyFRcXa+TIkSooKNDMmTO1d+/egDJz585VVlZWwM+ECRMCyrS0tKi0tFT5+fkaMWKEbr/9dn311VexfCumogdJogpjslRWogheWBPpBvTkMdp+93ol2ejlA8tiKBMAAEDkbNmyRffcc48uvvhinTp1Sj/72c80depUvf/++0pLO31PV1JSooqKCv/vTmfgF+KLFy/Wa6+9plWrVik7O1tLlizR9OnTtXnzZjksML8hCZJEFsZkqaxEEQNnLIksdb0scm+9fDokt0oX1ki7RylgaI/Ve/nAPOEkORjKBAAAEDlVVVUBv1dUVKigoEA7duzQuHHj/NtTUlI0bNiwLp+jvr5elZWVWrlypa655hpJ0rPPPquioiJt3rxZJSUlUYs/XgSVIPF6vbLZbNGKJW54vd6Af0Ot21t9r6/tcaP950xGp3K9PlcQr2tq3foGqcXdc5kUl5SZEVQc0dLbMZJ6OE7p6fJWbwx4v8ePH9fap57SZT9/XPaORFaKS0pPb+9V0ibT4dL+a5/USV/nyWHrtXzDE1o1fZEyBrVlxgbYncp0uAJf1+dtP7F7j9rr8wa8bleam5v15ptvqqSkpNdVe8Jq073G3X3MkTyXElE453A4nE6nbrrppoAY0L+E83nY74VzrQ2jblifS3GCdoVooW0hWsxqW4bRdkVvaGgIuA9PSUlRSkpKd9X8dSQp64wvz//85z+roKBAmZmZGjdunJYsWaKhQ4dKknbu3KnW1lYVFxf7y+fm5mrMmDHaunUrCZIz7d2713+QrKCmpiboOidOnJAk7du3TwMHDuyxbO2ptrLeU6ekLoZvdAyh+Nu+fWpK6vm5gnlds+o6GhtV+KP5svXShgybTZ8sf1re9PSgYomG3o6RFNpx+uzECR3p/MDBf/Qay8n2uie/PCJHfdvQqiZJh88ol3zsmMZIOuX1St3ta59Pye0xtx4/3mvMGzZs0PDhw6PapnuNu4eYI3kuJaKOdvX3v/9dR48eNTka9EehfB72d+Fca8OpG+nPJTPRrhAttC1ES6zbls1mU15enoqKitTY2OjfvmjRIj344IPd1jMMQw899JAuv/xyFRYW+rdfd911mjJlikaOHKnPP/9cjz32mCZPnqzNmzcrJSVFtbW1cjqdGtRp2gVJysnJUW1tbeTfYBwKKkFSUFBgmR4kNTU1GjVqVNDjrI63/xGTn59/VrbuTGnu49KXkiMpqev5LQyf5JHOz8/X11w9P1cwrxvJus3Nzbrpppt04YUX9tq7QIcOyWYYMjIzJXs37chnyFZfr1EjR0o5OUHFEg29HiMpqON05EhbWuS8887TkCFDgoqlz8fp0CFJUpLDIXXXftuz3+fn5/e6n2PWpnuLu4eYI3kuJaJw2hXQk3A+D/u9cK61YdSN9OeSGWhXiBbaFqLFrLZlGIY8Ho927dp1Vg+SnixcuFC7d+9WdXV1wPapU6f6/19YWKiLL75YF110kV5//XXdfPPNPcZhhTyAFGSCxOFwWGbHSG3vN9gToKN8X+o67G2P29p/zmTrVK7X5wridSNZNz09XVOmTOlb4fb3K7u9+z8I1fYHocPewx+NMdTbMZLi8Dh17Oc+RN2X/RyzNt1r3N3HHMlzKRGF066AvqBtdSGca20YdSP9uWQm2hWihbaFaIl12+oYvZGRkdHn+/DS0lJVV1dr48aNGjFiRI9lhw8frpEjR2rfvn2SpGHDhsnj8aiuri6gF8nhw4d12WWXhfguEgvL/AIx5HK59J3vfIeVOwAAAABEjGEYWrhwodavX6+1a9cqLy+v1zrHjh3TV199peHDh0uSxo4dq+TkZL399tv+MgcPHtSePXsskyBhFZt+giUzE0NqaqquuOKK3ocjAQAAAEAfLViwQFVVVXrxxReVnp7unzMkIyNDqampamxs1BNPPKGbb75Zw4cP1xdffKFHHnlEgwcP9k+6n5mZqTvuuENLlixRdna2srKytHTpUhUWFvpXtenvSJDEgY7J1Pq6vSssmQkAAAAA1rR69WpJ0qRJkwK2V1RUaMaMGXI4HPrkk0/029/+VvX19Ro2bJjGjx+v1atXByzE8NhjjykpKUmzZ8+W2+3WVVddpZdeeskyw9ZIkERYMD05Bjicssumo61NkqTkVunCGmn3KKk1ua2MXTYNcDijGTIAAAAAIIEd72VlytTUVK1Zs6bX53G5XCorK1NZWVmkQksoJEgiLJieHNnONB24rlwnvR5JUkNdnZ7a8LhemLFYGe2T4gxwOJXtTItavAASH3PbAAAAAOEjQWKybGeastWWADnubtuW6xqkrNT4W5YPQHxibhsAAAAgfKxiAwAAAAAALI8ECQAAAAAAsDwSJLCc5uZmrV27Vs3NzWaHAgAAAACIEyRIYDlut1vr16+X2+02OxQAAAAAQJwgQQIAAAAAACyPVWwAAABC5fOF9hgAAIg7JEgAAACC5UqRYbPJVl8vSWq22/VazlBdf+iwUjslRgybTXKlmBUlAAAIAgkSAACAYGVmyvv6HyR3iySpsb5ea5c9qXHLlik5M/N0OVeK1Pl3AAAQt0iQAAAAhCIzU+rIfTiT2/4dOkTKyjItJAAAEDomaQUAAAAAAJZHggQAAAAAAFgeCRIAAAAAAGB5zEECSzjmadJJr0eS1OCukyT9w12npubTZQY4nMp2ppkRHgAAAADAZCRI0O8d8zTpnDcekE+GJCm5VbpwtHTp1sfVmny6nF02HbiuPG6TJC6XS5MmTZLL5TI7FACAybyGL6THAABA90iQoN/feJ/0euSTocHJaXLY7JJTOjBW6rzGgNfw6WhrWy+TbMVngiQ1NVWTJ082OwwAgIkGOJyyy6ajrU2S2pP+NdLuUTor6T/A4TQpSgAAEhMJEljmxtths7clSAAASFDZzjQduK7cP2y0xe3We94/6Yqrxiul0xcdDBsFACB4JEgAAAASSLYz7XRvx1Qpf+r3zQ0IAIB+gq/T40h/H+oCAAAAAEC8ogdJHLHKUBcAAAAAAOINPUgAAAAAAIDlkSABAAAAAACWxxAboA+8hi+kx0zl6yGunh4DAAAAAAsiQQL0YIDDKbtsOtra5N+W3CpdWCPtHiW1Jrdts8umAQ6nSVGewZUiw2aTrb6+x2KGzSa5UmIUFADgLCSyAQCIKyRIgB5kO9N04LpynfR6/Nsa6ur01IbH9cKMxcoYNEhSWyIl25lmVpiBMjPlff0Pkrul53KuFCkzMzYxAQBOOyOR3Wy367Wcobr+0GGldkqMkMgGACC2SJAAvch2pilbp5Mfx91t/+a6BikrNcukqHqRmSmR+wCA+HRGIruxvl5rlz2pccuWKblz4ppENgAAMUWCBECg+vrA3icdQ3UOH5E8rW3/5492AAhP50S2s3285tAhUlacJt4BALAAEiQATquvl2PiDbIZhn9Tut2uyTlDlT7rLiW1d/02bLa2bz9JkgAAAADoJ0iQADjN3SKbYcjIzJTsbauAuyRNafFImZkyJMnnaxs3726JzjCe7iYmZMJCAAAAAFFkNzsAAHHIbpccjq5/7FG6bHSatNB2/Ljc9fX6fYpT7vbfbfX1TFgIAAAAdKG8vFzFxcUaOXKkCgoKNHPmTO3du7fb8vfff7+ysrL0zDPPBGyfNGmSsrKyAn7uvvvuaIcfN+hBAgTJ5XJp0qRJcrlcZocSt7xG1709utsuqW+TFjL3CQAAAHCWLVu26J577tHFF1+sU6dO6Wc/+5mmTp2q999/X2lpgattbtiwQR9++KFyc3O7fK677rpLixcv9v9upfseEiRAkFJTUzV58mSzw4hLAxxO2WXT0dYmSVJyq3RhjbR7lNTaPgehXTYNcDi7fgImLQQAAACCVlVVFfB7RUWFCgoKtGPHDo0bN86//cCBAyotLVVVVZVuu+22Lp8rNTV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\n",
      "text/plain": [
       "<Figure size 1380x690 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "K线图分析:\n",
      "期间价格变动: 2.35 (0.91%)\n",
      "平均成交量: 120,270\n",
      "整体趋势: 震荡走势\n",
      "日收益率波动率: 1.71%\n",
      "支撑位（期间最低价）: 239.02\n",
      "阻力位（期间最高价）: 277.11\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    # 确保索引是日期类型\n",
    "    if not isinstance(df.index, pd.DatetimeIndex):\n",
    "        print(\"索引不是日期类型，无法绘制K线图\")\n",
    "        raise ValueError(\"需要日期索引来绘制K线图\")\n",
    "    \n",
    "    # 筛选2023年4-6月数据\n",
    "    df_kline = df[(df.index.year == 2023) & (df.index.month >= 4) & (df.index.month <= 6)]\n",
    "    \n",
    "    if df_kline.empty:\n",
    "        print(\"未找到2023年4-6月的数据，使用部分数据进行演示\")\n",
    "        # 使用最近的60天数据\n",
    "        df_kline = df.tail(60)\n",
    "    \n",
    "    print(f\"K线图数据范围: {df_kline.index.min()} 到 {df_kline.index.max()}\")\n",
    "    \n",
    "    # 查找OHLC列\n",
    "    ohlc_cols = {}\n",
    "    possible_names = {\n",
    "        'open': ['开盘', 'open', 'Open'],\n",
    "        'high': ['最高', 'high', 'High'],\n",
    "        'low': ['最低', 'low', 'Low'],\n",
    "        'close': ['收盘', 'close', 'Close'],\n",
    "        'volume': ['成交量', 'volume', 'Volume']\n",
    "    }\n",
    "    \n",
    "    for key, keywords in possible_names.items():\n",
    "        found = False\n",
    "        for col in df_kline.columns:\n",
    "            if any(keyword in col.lower() for keyword in keywords):\n",
    "                ohlc_cols[key] = col\n",
    "                found = True\n",
    "                break\n",
    "        if not found and key != 'volume':  # 成交量是可选的\n",
    "            print(f\"警告: 未找到'{key}'列\")\n",
    "    \n",
    "    # 检查是否有足够的列来绘制K线图\n",
    "    required_cols = ['open', 'high', 'low', 'close']\n",
    "    if not all(col in ohlc_cols for col in required_cols):\n",
    "        print(\"未找到足够的OHLC列，使用默认列名\")\n",
    "        # 使用前四列数值列\n",
    "        numeric_cols = df_kline.select_dtypes(include=[np.number]).columns\n",
    "        if len(numeric_cols) >= 4:\n",
    "            ohlc_cols = {\n",
    "                'open': numeric_cols[0],\n",
    "                'high': numeric_cols[1],\n",
    "                'low': numeric_cols[2],\n",
    "                'close': numeric_cols[3]\n",
    "            }\n",
    "            if len(numeric_cols) >= 5:\n",
    "                ohlc_cols['volume'] = numeric_cols[4]\n",
    "        else:\n",
    "            raise ValueError(\"数据中没有足够的数值列来绘制K线图\")\n",
    "    \n",
    "    # 重命名列以适应mplfinance\n",
    "    kline_data = df_kline.rename(columns={\n",
    "        ohlc_cols['open']: 'Open',\n",
    "        ohlc_cols['high']: 'High',\n",
    "        ohlc_cols['low']: 'Low',\n",
    "        ohlc_cols['close']: 'Close'\n",
    "    })\n",
    "    \n",
    "    if 'volume' in ohlc_cols:\n",
    "        kline_data = kline_data.rename(columns={ohlc_cols['volume']: 'Volume'})\n",
    "    \n",
    "    # 绘制K线图\n",
    "    kwargs = {\n",
    "        'type': 'candle',\n",
    "        'style': 'yahoo',\n",
    "        'title': '比亚迪2023年4-6月K线图',\n",
    "        'ylabel': '价格',\n",
    "        'figratio': (14, 7),\n",
    "        'figscale': 1.2\n",
    "    }\n",
    "    \n",
    "    if 'Volume' in kline_data.columns:\n",
    "        kwargs['volume'] = True\n",
    "        kwargs['ylabel_lower'] = '成交量'\n",
    "    \n",
    "    mpf.plot(kline_data, **kwargs, savefig='2023年4-6月K线图.png')\n",
    "    print(\"2023年4-6月K线图已保存为'2023年4-6月K线图.png'\")\n",
    "    \n",
    "    # 显示K线图（在Notebook中）\n",
    "    mpf.plot(kline_data, **kwargs, show_nontrading=False)\n",
    "    \n",
    "    # K线图分析\n",
    "    print(\"\\nK线图分析:\")\n",
    "    # 计算基本统计量\n",
    "    price_change = kline_data['Close'].iloc[-1] - kline_data['Close'].iloc[0]\n",
    "    price_change_pct = (price_change / kline_data['Close'].iloc[0]) * 100\n",
    "    avg_volume = kline_data['Volume'].mean() if 'Volume' in kline_data.columns else None\n",
    "    \n",
    "    print(f\"期间价格变动: {price_change:.2f} ({price_change_pct:.2f}%)\")\n",
    "    if avg_volume:\n",
    "        print(f\"平均成交量: {avg_volume:,.0f}\")\n",
    "    \n",
    "    # 趋势分析\n",
    "    if price_change_pct > 5:\n",
    "        trend = \"上涨趋势\"\n",
    "    elif price_change_pct < -5:\n",
    "        trend = \"下跌趋势\"\n",
    "    else:\n",
    "        trend = \"震荡走势\"\n",
    "    \n",
    "    print(f\"整体趋势: {trend}\")\n",
    "    \n",
    "    # 波动性分析\n",
    "    daily_returns = kline_data['Close'].pct_change().dropna()\n",
    "    volatility = daily_returns.std() * 100\n",
    "    print(f\"日收益率波动率: {volatility:.2f}%\")\n",
    "    \n",
    "    # 支撑阻力位分析\n",
    "    support = kline_data['Low'].min()\n",
    "    resistance = kline_data['High'].max()\n",
    "    print(f\"支撑位（期间最低价）: {support:.2f}\")\n",
    "    print(f\"阻力位（期间最高价）: {resistance:.2f}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"绘制K线图时出错: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "比亚迪股票价格走势分析与可视化完成！\n",
      "生成的图表文件:\n",
      "1. 相关系数热力图.png\n",
      "2. 2023年收盘价时序图.png\n",
      "3. 2023年4-6月K线图.png\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n比亚迪股票价格走势分析与可视化完成！\")\n",
    "print(\"生成的图表文件:\")\n",
    "print(\"1. 相关系数热力图.png\")\n",
    "print(\"2. 2023年收盘价时序图.png\")\n",
    "print(\"3. 2023年4-6月K线图.png\")"
   ]
  }
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